mnist-number-recognistion.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2024-01-16T13:19:57.243739Z","iopub.status.busy":"2024-01-16T13:19:57.242833Z","iopub.status.idle":"2024-01-16T13:20:02.884056Z","shell.execute_reply":"2024-01-16T13:20:02.882399Z","shell.execute_reply.started":"2024-01-16T13:19:57.243699Z"},"trusted":true},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","from matplotlib import pyplot as plt\n","\n","data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2024-01-16T13:20:18.665467Z","iopub.status.busy":"2024-01-16T13:20:18.664366Z","iopub.status.idle":"2024-01-16T13:20:19.733720Z","shell.execute_reply":"2024-01-16T13:20:19.732412Z","shell.execute_reply.started":"2024-01-16T13:20:18.665417Z"},"trusted":true},"outputs":[],"source":["data = np.array(data)\n","m, n = data.shape\n","np.random.shuffle(data)\n","\n","data_dev = data[0:1000].T\n","Y_dev = data_dev[0]\n","X_dev = data_dev[1:n]\n","X_dev = X_dev / 255.\n","\n","data_train = data[1000:m].T\n","Y_train = data_train[0]\n","X_train = data_train[1:n]\n","X_train = X_train / 255.\n","_,m_train = X_train.shape"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2024-01-16T13:20:49.477438Z","iopub.status.busy":"2024-01-16T13:20:49.476962Z","iopub.status.idle":"2024-01-16T13:20:49.494581Z","shell.execute_reply":"2024-01-16T13:20:49.493412Z","shell.execute_reply.started":"2024-01-16T13:20:49.477396Z"},"trusted":true},"outputs":[],"source":["def init_params():\n","    W1 = np.random.rand(10, 784) - 0.5\n","    b1 = np.random.rand(10, 1) - 0.5\n","    W2 = np.random.rand(10, 10) - 0.5\n","    b2 = np.random.rand(10, 1) - 0.5\n","    return W1, b1, W2, b2\n","\n","def ReLU(Z):\n","    return np.maximum(Z, 0)\n","\n","def softmax(Z):\n","    A = np.exp(Z) / sum(np.exp(Z))\n","    return A\n","    \n","def forward_prop(W1, b1, W2, b2, X):\n","    Z1 = W1.dot(X) + b1\n","    A1 = ReLU(Z1)\n","    Z2 = W2.dot(A1) + b2\n","    A2 = softmax(Z2)\n","    return Z1, A1, Z2, A2\n","\n","def ReLU_deriv(Z):\n","    return Z > 0\n","\n","def one_hot(Y):\n","    one_hot_Y = np.zeros((Y.size, Y.max() + 1))\n","    one_hot_Y[np.arange(Y.size), Y] = 1\n","    one_hot_Y = one_hot_Y.T\n","    return one_hot_Y\n","\n","def backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y):\n","    one_hot_Y = one_hot(Y)\n","    dZ2 = A2 - one_hot_Y\n","    dW2 = 1 / m * dZ2.dot(A1.T)\n","    db2 = 1 / m * np.sum(dZ2)\n","    dZ1 = W2.T.dot(dZ2) * ReLU_deriv(Z1)\n","    dW1 = 1 / m * dZ1.dot(X.T)\n","    db1 = 1 / m * np.sum(dZ1)\n","    return dW1, db1, dW2, db2\n","\n","def update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha):\n","    W1 = W1 - alpha * dW1\n","    b1 = b1 - alpha * db1    \n","    W2 = W2 - alpha * dW2  \n","    b2 = b2 - alpha * db2    \n","    return W1, b1, W2, b2"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-01-16T13:21:14.524412Z","iopub.status.busy":"2024-01-16T13:21:14.523942Z","iopub.status.idle":"2024-01-16T13:21:14.536041Z","shell.execute_reply":"2024-01-16T13:21:14.534201Z","shell.execute_reply.started":"2024-01-16T13:21:14.524375Z"},"trusted":true},"outputs":[],"source":["def get_predictions(A2):\n","    return np.argmax(A2, 0)\n","\n","def get_accuracy(predictions, Y):\n","    print(predictions, Y)\n","    return np.sum(predictions == Y) / Y.size\n","\n","def gradient_descent(X, Y, alpha, iterations):\n","    W1, b1, W2, b2 = init_params()\n","    for i in range(iterations):\n","        Z1, A1, Z2, A2 = forward_prop(W1, b1, W2, b2, X)\n","        dW1, db1, dW2, db2 = backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y)\n","        W1, b1, W2, b2 = update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha)\n","        if i % 10 == 0:\n","            print(\"Iteration: \", i)\n","            predictions = get_predictions(A2)\n","            print(get_accuracy(predictions, Y))\n","    return W1, b1, W2, b2"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-01-16T13:21:31.523095Z","iopub.status.busy":"2024-01-16T13:21:31.522447Z","iopub.status.idle":"2024-01-16T13:22:57.716064Z","shell.execute_reply":"2024-01-16T13:22:57.714312Z","shell.execute_reply.started":"2024-01-16T13:21:31.523052Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Iteration:  0\n","[4 9 9 ... 3 9 7] [3 9 0 ... 1 9 2]\n","0.08531707317073171\n","Iteration:  10\n","[5 6 8 ... 3 6 2] [3 9 0 ... 1 9 2]\n","0.1692439024390244\n","Iteration:  20\n","[5 6 8 ... 3 6 2] [3 9 0 ... 1 9 2]\n","0.2430731707317073\n","Iteration:  30\n","[5 6 8 ... 3 8 2] [3 9 0 ... 1 9 2]\n","0.31241463414634146\n","Iteration:  40\n","[5 9 8 ... 3 7 2] [3 9 0 ... 1 9 2]\n","0.37826829268292683\n","Iteration:  50\n","[5 9 8 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.43285365853658536\n","Iteration:  60\n","[5 9 9 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.48004878048780486\n","Iteration:  70\n","[5 9 0 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.525219512195122\n","Iteration:  80\n","[5 0 0 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.5638292682926829\n","Iteration:  90\n","[5 0 0 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.5957560975609756\n","Iteration:  100\n","[5 0 0 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.6203414634146341\n","Iteration:  110\n","[5 0 0 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.639\n","Iteration:  120\n","[3 0 0 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.6547560975609756\n","Iteration:  130\n","[3 0 0 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.6702926829268293\n","Iteration:  140\n","[3 0 0 ... 4 7 2] [3 9 0 ... 1 9 2]\n","0.6825121951219513\n","Iteration:  150\n","[3 7 0 ... 4 9 2] [3 9 0 ... 1 9 2]\n","0.6945365853658536\n","Iteration:  160\n","[3 7 0 ... 4 9 2] [3 9 0 ... 1 9 2]\n","0.7041219512195122\n","Iteration:  170\n","[3 7 0 ... 4 9 2] [3 9 0 ... 1 9 2]\n","0.713170731707317\n","Iteration:  180\n","[3 7 0 ... 4 9 2] [3 9 0 ... 1 9 2]\n","0.7210975609756097\n","Iteration:  190\n","[3 7 0 ... 4 9 2] [3 9 0 ... 1 9 2]\n","0.7293170731707317\n","Iteration:  200\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7364878048780488\n","Iteration:  210\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7434634146341463\n","Iteration:  220\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.750390243902439\n","Iteration:  230\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7561707317073171\n","Iteration:  240\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7617317073170732\n","Iteration:  250\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7678292682926829\n","Iteration:  260\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7727073170731708\n","Iteration:  270\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7775121951219512\n","Iteration:  280\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7827317073170732\n","Iteration:  290\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.786609756097561\n","Iteration:  300\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7909512195121952\n","Iteration:  310\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.7950243902439025\n","Iteration:  320\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.798609756097561\n","Iteration:  330\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8018048780487805\n","Iteration:  340\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8047804878048781\n","Iteration:  350\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8078536585365854\n","Iteration:  360\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8101707317073171\n","Iteration:  370\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8130487804878048\n","Iteration:  380\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8153170731707317\n","Iteration:  390\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8178780487804878\n","Iteration:  400\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.819780487804878\n","Iteration:  410\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.822\n","Iteration:  420\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.824390243902439\n","Iteration:  430\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8261219512195122\n","Iteration:  440\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8280243902439024\n","Iteration:  450\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8302439024390244\n","Iteration:  460\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.831780487804878\n","Iteration:  470\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8331219512195122\n","Iteration:  480\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8348536585365853\n","Iteration:  490\n","[3 7 0 ... 1 9 2] [3 9 0 ... 1 9 2]\n","0.8362439024390244\n"]}],"source":["W1, b1, W2, b2 = gradient_descent(X_train, Y_train, 0.10, 500)"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-01-16T13:23:38.283856Z","iopub.status.busy":"2024-01-16T13:23:38.283465Z","iopub.status.idle":"2024-01-16T13:23:38.292658Z","shell.execute_reply":"2024-01-16T13:23:38.291584Z","shell.execute_reply.started":"2024-01-16T13:23:38.283826Z"},"trusted":true},"outputs":[],"source":["def make_predictions(X, W1, b1, W2, b2):\n","    _, _, _, A2 = forward_prop(W1, b1, W2, b2, X)\n","    predictions = get_predictions(A2)\n","    return predictions\n","\n","def test_prediction(index, W1, b1, W2, b2):\n","    current_image = X_train[:, index, None]\n","    prediction = make_predictions(X_train[:, index, None], W1, b1, W2, b2)\n","    label = Y_train[index]\n","    print(\"Prediction: \", prediction)\n","    print(\"Label: \", label)\n","    \n","    current_image = current_image.reshape((28, 28)) * 255\n","    plt.gray()\n","    plt.imshow(current_image, interpolation='nearest')\n","    plt.show()"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-01-16T13:23:50.005614Z","iopub.status.busy":"2024-01-16T13:23:50.005191Z","iopub.status.idle":"2024-01-16T13:23:51.084687Z","shell.execute_reply":"2024-01-16T13:23:51.083264Z","shell.execute_reply.started":"2024-01-16T13:23:50.005581Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Prediction:  [3]\n","Label:  3\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAbgklEQVR4nO3df2xV9f3H8dctP64I7cVa2ttKgYIKi/xYZNI1aMXR0dYfE2EG1Gy4GRFXzKRTli5T/LGlkyWbc0FdMgOaib8WgUkMCxZaIhYMKCHuR0ebOkqgRUl6bym2IP3sD77er1da6rnc2/ft5flIPknvOefd8+b0cF89955+rs855wQAwABLs24AAHBhIoAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgYqh1A1/V09Ojw4cPKz09XT6fz7odAIBHzjl1dHQoLy9PaWl9X+ckXQAdPnxY+fn51m0AAM5TS0uLxo4d2+f6pHsJLj093boFAEAc9Pd8nrAAWrNmjSZMmKCLLrpIhYWFev/9979WHS+7AUBq6O/5PCEB9Nprr6myslKrVq3SBx98oBkzZqi0tFRHjx5NxO4AAIORS4BZs2a5ioqKyOPTp0+7vLw8V11d3W9tKBRykhgMBoMxyEcoFDrn833cr4BOnjypvXv3qqSkJLIsLS1NJSUlqq+vP2v77u5uhcPhqAEASH1xD6BPP/1Up0+fVk5OTtTynJwctba2nrV9dXW1AoFAZHAHHABcGMzvgquqqlIoFIqMlpYW65YAAAMg7n8HlJWVpSFDhqitrS1qeVtbm4LB4Fnb+/1++f3+eLcBAEhycb8CGj58uGbOnKmamprIsp6eHtXU1KioqCjeuwMADFIJmQmhsrJSS5Ys0be+9S3NmjVLTz/9tDo7O/WjH/0oEbsDAAxCCQmgRYsW6ZNPPtGjjz6q1tZWffOb39SWLVvOujEBAHDh8jnnnHUTXxYOhxUIBKzbAACcp1AopIyMjD7Xm98FBwC4MBFAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwMRQ6waA/syZM8dzzapVq2La1/XXX++5xufzea5xznmuicWhQ4diqvvrX//quebNN9/0XPPuu+96rkHq4AoIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACZ8bqFkRv6ZwOKxAIGDdBr6Giy++2HPNO++847nmkksu8VwzkJJ5MtKRI0fGVHfZZZd5runp6fFc8+tf/9pzzRNPPOG5JpbecP5CoZAyMjL6XM8VEADABAEEADAR9wB67LHH5PP5osaUKVPivRsAwCCXkA+ku+qqq6Je6x86lM+9AwBES0gyDB06VMFgMBHfGgCQIhLyHtCBAweUl5eniRMn6q677tLBgwf73La7u1vhcDhqAABSX9wDqLCwUOvWrdOWLVv03HPPqbm5Wdddd506Ojp63b66ulqBQCAy8vPz490SACAJxT2AysvLdfvtt2v69OkqLS3V22+/rfb2dr3++uu9bl9VVaVQKBQZLS0t8W4JAJCEEn53wOjRo3XllVeqsbGx1/V+v19+vz/RbQAAkkzC/w7o+PHjampqUm5ubqJ3BQAYROIeQA899JDq6ur08ccf67333tNtt92mIUOG6I477oj3rgAAg1jcX4I7dOiQ7rjjDh07dkxjxozRtddeq127dmnMmDHx3hUAYBBjMlLEbNSoUZ5rnnrqKc81K1as8Fxz8uRJzzWpKNaJXP/whz94rrnrrrti2pdXsZx3n332WQI6QX+YjBQAkJQIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYYDJSAGe5+eabPdds2rQpAZ2cjclIBw8mIwUAJCUCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgImh1g0ASJzFixfHVPfCCy94rvn888891zz77LOea7q6ujzXIDlxBQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEk5ECBoYMGeK5pqioyHPNmjVrPNdI0tCh3p8a3nnnHc81K1as8FyD1MEVEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABNMRgqcp1mzZnmueeSRRzzX3HjjjZ5rYvXMM894rmFiUXjFFRAAwAQBBAAw4TmAduzYoVtuuUV5eXny+XzauHFj1HrnnB599FHl5uZqxIgRKikp0YEDB+LVLwAgRXgOoM7OTs2YMaPPD7pavXq1nnnmGT3//PPavXu3Ro4cqdLSUnV1dZ13swCA1OH5JoTy8nKVl5f3us45p6efflq//OUvdeutt0qSXnrpJeXk5Gjjxo1avHjx+XULAEgZcX0PqLm5Wa2trSopKYksCwQCKiwsVH19fa813d3dCofDUQMAkPriGkCtra2SpJycnKjlOTk5kXVfVV1drUAgEBn5+fnxbAkAkKTM74KrqqpSKBSKjJaWFuuWAAADIK4BFAwGJUltbW1Ry9va2iLrvsrv9ysjIyNqAABSX1wDqKCgQMFgUDU1NZFl4XBYu3fvVlFRUTx3BQAY5DzfBXf8+HE1NjZGHjc3N2vfvn3KzMzUuHHj9OCDD+pXv/qVrrjiChUUFOiRRx5RXl6e5s+fH8++AQCDnOcA2rNnj2644YbI48rKSknSkiVLtG7dOq1cuVKdnZ1aunSp2tvbde2112rLli266KKL4tc1AGDQ8znnnHUTXxYOhxUIBKzbQBJJT0/3XHPHHXfEtK+VK1d6rhk3bpznmiFDhniuOXnypOea73//+55rJOntt9/2XJNkTyVIAqFQ6Jzv65vfBQcAuDARQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEx4/jgG4AtZWVmea+677z7PNT/+8Y8910yYMMFzTax8Pp/nmlhmjh461Pt/19tvv91zjSTt2LHDc01HR0dM+8KFiysgAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJpiMFDH7wQ9+4LnmiSeeSEAn8dPU1OS5ZufOnQno5Gzl5eWea2L5GUnSmDFjPNfcdNNNMe0LFy6ugAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJhgMlLEbPv27QNS88knn3iuue+++zzXSFI4HI6pbiDMnz/fc82bb74Z075imfi0tLTUc83f//53zzVIHVwBAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMOFzzjnrJr4sHA4rEAhYtwEknbQ0778vvvjiizHt68477/Rc09LS4rlmwoQJnmsweIRCIWVkZPS5nisgAIAJAggAYMJzAO3YsUO33HKL8vLy5PP5tHHjxqj1d999t3w+X9QoKyuLV78AgBThOYA6Ozs1Y8YMrVmzps9tysrKdOTIkch45ZVXzqtJAEDq8fyJqOXl5f1+WqLf71cwGIy5KQBA6kvIe0C1tbXKzs7W5MmTdf/99+vYsWN9btvd3a1wOBw1AACpL+4BVFZWppdeekk1NTV66qmnVFdXp/Lycp0+fbrX7aurqxUIBCIjPz8/3i0BAJKQ55fg+rN48eLI19OmTdP06dM1adIk1dbWau7cuWdtX1VVpcrKysjjcDhMCAHABSDht2FPnDhRWVlZamxs7HW93+9XRkZG1AAApL6EB9ChQ4d07Ngx5ebmJnpXAIBBxPNLcMePH4+6mmlubta+ffuUmZmpzMxMPf7441q4cKGCwaCampq0cuVKXX755SotLY1r4wCAwc1zAO3Zs0c33HBD5PEX798sWbJEzz33nPbv368XX3xR7e3tysvL07x58/Tkk0/K7/fHr2sAwKDHZKRACrv55ptjqtu0aZPnmnP9uUVfsrOzPddg8GAyUgBAUiKAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmIj7R3IDSB7FxcUDtq9Dhw4N2L6QGrgCAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYILJSKERI0bEVDd9+nTPNUOHej/ldu7c6bkmFaWlef99MZafUay2bds2YPtCauAKCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkmI00xU6ZM8Vzz6quvxrSv/Px8zzXf+973YtoXpKuvvtpzzXe/+92Y9vX55597rtm4cWNM+8KFiysgAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJpiMNIkNHer9x/PCCy94rpk2bZrnGkm6+eabPdfs3Lkzpn2lmtmzZ3uuefbZZxPQSe/+8Y9/eK559913E9AJUhlXQAAAEwQQAMCEpwCqrq7WNddco/T0dGVnZ2v+/PlqaGiI2qarq0sVFRW69NJLNWrUKC1cuFBtbW1xbRoAMPh5CqC6ujpVVFRo165d2rp1q06dOqV58+aps7Mzss2KFSv01ltv6Y033lBdXZ0OHz6sBQsWxL1xAMDg5uld7i1btkQ9XrdunbKzs7V3714VFxcrFArphRde0Pr16/Wd73xHkrR27Vp94xvf0K5du/Ttb387fp0DAAa183oPKBQKSZIyMzMlSXv37tWpU6dUUlIS2WbKlCkaN26c6uvre/0e3d3dCofDUQMAkPpiDqCenh49+OCDmj17tqZOnSpJam1t1fDhwzV69OiobXNyctTa2trr96murlYgEIiM/Pz8WFsCAAwiMQdQRUWFPvroI7366qvn1UBVVZVCoVBktLS0nNf3AwAMDjH9Iery5cu1efNm7dixQ2PHjo0sDwaDOnnypNrb26Ougtra2hQMBnv9Xn6/X36/P5Y2AACDmKcrIOecli9frg0bNmjbtm0qKCiIWj9z5kwNGzZMNTU1kWUNDQ06ePCgioqK4tMxACAleLoCqqio0Pr167Vp0yalp6dH3tcJBAIaMWKEAoGA7rnnHlVWViozM1MZGRl64IEHVFRUxB1wAIAongLoueeekyTNmTMnavnatWt19913S5J+//vfKy0tTQsXLlR3d7dKS0sHdA4rAMDg4HPOOesmviwcDisQCFi3kRRimYz0b3/7m+ea0tJSzzWSYrplfunSpZ5rBvLGlFiOxcKFCz3XTJ482XNNLOfDtm3bPNdIsf2b+BMKfFUoFFJGRkaf65kLDgBgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABggtmwU8zIkSM917z++usx7ausrCymulTj8/k817S3t3uuefLJJz3X/PnPf/ZcI0kdHR0x1QFfxmzYAICkRAABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwASTkULp6ekx1f3whz/0XLNw4ULPNX6/33PNf/7zH881sXrvvfc812zdutVzzccff+y5BrDEZKQAgKREAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABJORAgASgslIAQBJiQACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJjwFUHV1ta655hqlp6crOztb8+fPV0NDQ9Q2c+bMkc/nixrLli2La9MAgMHPUwDV1dWpoqJCu3bt0tatW3Xq1CnNmzdPnZ2dUdvde++9OnLkSGSsXr06rk0DAAa/oV423rJlS9TjdevWKTs7W3v37lVxcXFk+cUXX6xgMBifDgEAKem83gMKhUKSpMzMzKjlL7/8srKysjR16lRVVVXpxIkTfX6P7u5uhcPhqAEAuAC4GJ0+fdrddNNNbvbs2VHL//SnP7ktW7a4/fv3u7/85S/usssuc7fddluf32fVqlVOEoPBYDBSbIRCoXPmSMwBtGzZMjd+/HjX0tJyzu1qamqcJNfY2Njr+q6uLhcKhSKjpaXF/KAxGAwG4/xHfwHk6T2gLyxfvlybN2/Wjh07NHbs2HNuW1hYKElqbGzUpEmTzlrv9/vl9/tjaQMAMIh5CiDnnB544AFt2LBBtbW1Kigo6Ldm3759kqTc3NyYGgQApCZPAVRRUaH169dr06ZNSk9PV2trqyQpEAhoxIgRampq0vr163XjjTfq0ksv1f79+7VixQoVFxdr+vTpCfkHAAAGKS/v+6iP1/nWrl3rnHPu4MGDrri42GVmZjq/3+8uv/xy9/DDD/f7OuCXhUIh89ctGQwGg3H+o7/nft//BUvSCIfDCgQC1m0AAM5TKBRSRkZGn+uZCw4AYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYCLpAsg5Z90CACAO+ns+T7oA6ujosG4BABAH/T2f+1ySXXL09PTo8OHDSk9Pl8/ni1oXDoeVn5+vlpYWZWRkGHVoj+NwBsfhDI7DGRyHM5LhODjn1NHRoby8PKWl9X2dM3QAe/pa0tLSNHbs2HNuk5GRcUGfYF/gOJzBcTiD43AGx+EM6+MQCAT63SbpXoIDAFwYCCAAgIlBFUB+v1+rVq2S3++3bsUUx+EMjsMZHIczOA5nDKbjkHQ3IQAALgyD6goIAJA6CCAAgAkCCABgggACAJgYNAG0Zs0aTZgwQRdddJEKCwv1/vvvW7c04B577DH5fL6oMWXKFOu2Em7Hjh265ZZblJeXJ5/Pp40bN0atd87p0UcfVW5urkaMGKGSkhIdOHDAptkE6u843H333WedH2VlZTbNJkh1dbWuueYapaenKzs7W/Pnz1dDQ0PUNl1dXaqoqNCll16qUaNGaeHChWprazPqODG+znGYM2fOWefDsmXLjDru3aAIoNdee02VlZVatWqVPvjgA82YMUOlpaU6evSodWsD7qqrrtKRI0ci491337VuKeE6Ozs1Y8YMrVmzptf1q1ev1jPPPKPnn39eu3fv1siRI1VaWqqurq4B7jSx+jsOklRWVhZ1frzyyisD2GHi1dXVqaKiQrt27dLWrVt16tQpzZs3T52dnZFtVqxYobfeektvvPGG6urqdPjwYS1YsMCw6/j7OsdBku69996o82H16tVGHffBDQKzZs1yFRUVkcenT592eXl5rrq62rCrgbdq1So3Y8YM6zZMSXIbNmyIPO7p6XHBYND99re/jSxrb293fr/fvfLKKwYdDoyvHgfnnFuyZIm79dZbTfqxcvToUSfJ1dXVOefO/OyHDRvm3njjjcg2//rXv5wkV19fb9Vmwn31ODjn3PXXX+9++tOf2jX1NST9FdDJkye1d+9elZSURJalpaWppKRE9fX1hp3ZOHDggPLy8jRx4kTdddddOnjwoHVLppqbm9Xa2hp1fgQCARUWFl6Q50dtba2ys7M1efJk3X///Tp27Jh1SwkVCoUkSZmZmZKkvXv36tSpU1Hnw5QpUzRu3LiUPh++ehy+8PLLLysrK0tTp05VVVWVTpw4YdFen5JuMtKv+vTTT3X69Gnl5ORELc/JydG///1vo65sFBYWat26dZo8ebKOHDmixx9/XNddd50++ugjpaenW7dnorW1VZJ6PT++WHehKCsr04IFC1RQUKCmpib94he/UHl5uerr6zVkyBDr9uKup6dHDz74oGbPnq2pU6dKOnM+DB8+XKNHj47aNpXPh96OgyTdeeedGj9+vPLy8rR//379/Oc/V0NDg958803DbqMlfQDh/5WXl0e+nj59ugoLCzV+/Hi9/vrruueeeww7QzJYvHhx5Otp06Zp+vTpmjRpkmprazV37lzDzhKjoqJCH3300QXxPui59HUcli5dGvl62rRpys3N1dy5c9XU1KRJkyYNdJu9SvqX4LKysjRkyJCz7mJpa2tTMBg06io5jB49WldeeaUaGxutWzHzxTnA+XG2iRMnKisrKyXPj+XLl2vz5s3avn171Me3BINBnTx5Uu3t7VHbp+r50Ndx6E1hYaEkJdX5kPQBNHz4cM2cOVM1NTWRZT09PaqpqVFRUZFhZ/aOHz+upqYm5ebmWrdipqCgQMFgMOr8CIfD2r179wV/fhw6dEjHjh1LqfPDOafly5drw4YN2rZtmwoKCqLWz5w5U8OGDYs6HxoaGnTw4MGUOh/6Ow692bdvnyQl1/lgfRfE1/Hqq686v9/v1q1b5/75z3+6pUuXutGjR7vW1lbr1gbUz372M1dbW+uam5vdzp07XUlJicvKynJHjx61bi2hOjo63Icffug+/PBDJ8n97ne/cx9++KH773//65xz7je/+Y0bPXq027Rpk9u/f7+79dZbXUFBgfvss8+MO4+vcx2Hjo4O99BDD7n6+nrX3Nzs3nnnHXf11Ve7K664wnV1dVm3Hjf333+/CwQCrra21h05ciQyTpw4Edlm2bJlbty4cW7btm1uz549rqioyBUVFRl2HX/9HYfGxkb3xBNPuD179rjm5ma3adMmN3HiRFdcXGzcebRBEUDOOffHP/7RjRs3zg0fPtzNmjXL7dq1y7qlAbdo0SKXm5vrhg8f7i677DK3aNEi19jYaN1Wwm3fvt1JOmssWbLEOXfmVuxHHnnE5eTkOL/f7+bOnesaGhpsm06Acx2HEydOuHnz5rkxY8a4YcOGufHjx7t777035X5J6+3fL8mtXbs2ss1nn33mfvKTn7hLLrnEXXzxxe62225zR44csWs6Afo7DgcPHnTFxcUuMzPT+f1+d/nll7uHH37YhUIh28a/go9jAACYSPr3gAAAqYkAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAICJ/wEpUdAw9u5VEQAAAABJRU5ErkJggg==","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Prediction:  [7]\n","Label:  9\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Prediction:  [0]\n","Label:  0\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Prediction:  [9]\n","Label:  8\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["test_prediction(0, W1, b1, W2, b2)\n","test_prediction(1, W1, b1, W2, b2)\n","test_prediction(2, W1, b1, W2, b2)\n","test_prediction(3, W1, b1, W2, b2)"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2024-01-16T13:24:24.051977Z","iopub.status.busy":"2024-01-16T13:24:24.051500Z","iopub.status.idle":"2024-01-16T13:24:24.097114Z","shell.execute_reply":"2024-01-16T13:24:24.095393Z","shell.execute_reply.started":"2024-01-16T13:24:24.051943Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["[6 1 7 1 1 8 0 9 3 5 0 2 8 1 6 4 8 9 5 8 7 2 2 6 0 3 3 0 8 4 9 8 8 0 4 3 8\n"," 4 2 6 2 0 1 4 8 6 4 3 8 7 3 0 0 9 4 1 9 8 7 6 6 4 1 0 6 6 2 3 3 7 9 5 3 6\n"," 9 2 8 9 7 6 7 8 2 6 6 0 4 7 0 2 4 7 1 4 8 1 8 3 1 4 1 2 4 8 1 5 8 3 8 1 4\n"," 5 9 9 1 3 6 5 9 6 1 3 5 6 8 6 8 7 3 9 7 1 7 8 8 3 7 1 7 0 0 5 5 9 5 0 9 3\n"," 4 4 9 1 8 8 4 4 7 2 6 1 1 8 9 1 3 7 9 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8]\n"]},{"data":{"text/plain":["0.866"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["dev_predictions = make_predictions(X_dev, W1, b1, W2, b2)\n","get_accuracy(dev_predictions, Y_dev)"]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"databundleVersionId":861823,"sourceId":3004,"sourceType":"competition"}],"dockerImageVersionId":30635,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":4}